1. Field of the Invention
The present invention relates generally to the art of medical diagnostic imaging and baggage imaging and in particular to a method and apparatus for a computer tomography of the type wherein the measurement data are compressed and possibly decompressed en route to the computer that reconstructs images of the scanned object.
2. Description of Relevant Art
In Computer Tomography (CT), raw data is acquired during a scan of an object irradiated by X-rays from an X-ray source from different directions (projections) and the X-rays attenuated by the object are incident on a number of sensors (channels) that are arranged in multiple rows of a radiation detector. The number of rows is typically referred to as the number of slices.
The X-ray source and the detector are mounted on a rotor or gantry. Accordingly, for every rotor position, multiple vectors of data corresponding to the number of slices, also known as “fan” or “reading” are obtained. During a scan, the rotor is rotated in angular steps, each step giving a new reading. The set of readings for one full turn of the rotor is referred to as “rotation”. During or after a full rotation, the object to be examined (patient) is moved in a direction orthogonal to the rotors plane and data accumulated over many rotations is combined into one file that is accompanied by a file meta-data and additional meta-data for each vector.
In CT scans having 672 sensors with 128 slices there are 672*128 channels and may be 2320 readings per rotation. Accordingly, 199557120 measurements per rotation are obtained with each measured value stored as an unsigned word of 16 bit (2 bytes). Thus, there are roughly 400 MByte of data per rotation. The number of rotations depends on the size of the patient to be scanned, (for example approximately 10 to 20 rotations are required for scanning a human head). As such, a substantially high rate of data is generated that in turn is transmitted to a central processing unit for a reconstruction of the scanned object's images.
Therefore, compression methods capable of efficiently compressing this data en route to the computer can improve on the overall operation of CT imaging devices by reducing the storage space and bandwidth required for transmission of the data to the central processing unit.
CT imaging as a medical tool is well known for its benefits in medical analysis but at the same time, due to the use of X-rays, it is also known as harmful to the human (or other live object) being scanned. For this reason, in order to maximize the medical benefit from the minimal harm done, regulation requires that meaningful data obtained from a scan should not be lost. Sources of data loss may include lossless compression techniques as well as data corruption typically inevitable due to physical limitations of transmission means. Such transmission means typically ensure bit error rates below 1 part in 1,000,000,000,000 (10 to the power of 12) but not zero. For the size of data passed in a CT scan and calculated above (4 GByte), this implies that one in every few tens of scans may suffer one or more bit corruptions.
Due to the requirement not to lose meaningful data, compression algorithms for CT image data are preferably, albeit not exclusively, lossless compression algorithms. In addition, error detection and correction schemes should be used in order to protect against the unfortunate event of data corruption.
Several compression algorithms for CT imaging data have been proposed in the art. Most such algorithms, if not all of them, consist of three main steps: A first step where a prediction is made regarding the anticipated data, a second step where the data to be compressed is compared to the prediction and a third step where the difference between the data to be compressed and the predicted data is encoded as efficiently as possible (typically called the entropy encoding step).
Typical algorithms predict data based on previously stored data, and frequently encode image pixels based on multiple image pixels from the past. For example, the LOCO-I algorithm as disclosed in the U.S. Pat. No. 5,764,374 encodes pixels based on a group of pixels adjacent to the predicted pixel. As a result, the corruption of a single bit in compressed data, may affect more than a single pixel within the represented data because during decompression, a corrupted pixel may affect pixels surrounding it. Therefore, in typical compression algorithms, compressed data is more sensitive to the inevitable data corruption than non-compressed data. On the other hand, error detection and correction schemes, such as Reed Solomon or Hamming codes require the addition of significant data to the protected data, reducing the effectiveness of compression.
Thus, a need arises to find a technique for compression resulting in compressed data that is as sensitive to data corruption as non-compressed data while preserving high lossless compression ratios.
A slip ring (in electrical engineering terms) is an apparatus for making an electrical connection through a rotating assembly. Slip rings are also referred to as rotary electrical interfaces, rotating electrical connectors, collectors, swivels or electrical rotary joints. A slip ring consists of a conductive circle or band mounted on a shaft and insulated from it. Electrical connections from the rotating part of the system, such as the rotor of a generator, are made to the ring. Fixed contacts or brushes run in contact with the ring, transferring electrical power or signals to the exterior, static part of the system. In essence, a slip ring is the equivalent of a power and communications cable with the ability to traverse a rotating joint without getting tangled up. Furthermore contactless data and power transmission systems are used, herein also referred as slip rings. An example is disclosed in US 20030185427 A1.
Slip rings are frequently used in CT scanners in order to connect between the rotor of the scanner which generates CT raw data and the stationary central processing unit which is used to reconstruct and display 3D images from this data.
Current slip rings are limited in their data throughput to about 10 Gigabits per second (10 Gbps which is 10E9 bits per second). For some high resolution CT scans this bandwidth isn't sufficient in order to transfer raw data and display CT images in real time on the stationary display. Hence, compression is useful in virtually expanding the communications capacity of a slip ring.
Compression algorithms are by definition dependent on the data they need to compress. It is well known in the art that for any compression algorithm there exists data which cannot be compressed at all and in particular cannot be compressed above a given threshold ratio. Hence, a system that is designed to be dependent on a minimal compression ratio that can always be achieved may fail when data to be compressed fails to compress at the required ratio. This is true in particular to CT scanners that employ compression.
Without prior knowledge of scanned objects and the data generated by scanning them with a CT scanner, a CT scanner design would need to assume compression may not be effective at all, and hence taking worst case scenarios into consideration would eliminate the main benefit of compression—the ability to use relatively low throughput communications links for high bandwidth communications.